CODEWITHSUNDEEP
r
Rafael Díaz
Rafael Díaz

Reputation: 2289

How to create the confusion matrix with stargazer and caret

I'm trying to create a one table in sweave r, but it does not come out as I want. This is the code I have.

<<results='asis'>>=
## 2 class example
library(caret)
lvs <- c("normal", "abnormal")
truth <- factor(rep(lvs, times = c(86, 258)),
                levels = rev(lvs))

pred <- factor(c(rep(lvs, times = c(54, 32)),
                 rep(lvs, times = c(27, 231))),               
                 levels = rev(lvs))


xtab <- table(pred, truth)
Con.Mat <- confusionMatrix(xtab)
Con.Mat$table
Con.Mat$overall
Con.Mat$byClass

stargazer::stargazer(Con.Mat$table,head=FALSE,title = "Table")
stargazer::stargazer(Con.Mat$overall,head=FALSE,title = "overall")
stargazer::stargazer(Con.Mat$byClass,head=FALSE,title = "byClass")
@

Upvotes: 1

Views: 1465

Answers (2)

Santiago Capobianco
Santiago Capobianco

Reputation: 886

I came across the same problem and found the following workaround:

1) First you must convert the confusion matrix from class table to dataframe with as.data.frame.matrix().

ConfMat <- as.data.frame.matrix(Con.Mat$table)

2) Then, you can force Stargazer to produce the output as a data frame, with summary = FALSE argument.

stargazer(ConfMat, head = FALSE, title = "Table", summary = FALSE)

Later, you can add columns or row total by creating them in the dataframe. Also, add inbetween rows with percentages.

Hope it helps!

Upvotes: 1

Rafael D&#237;az
Rafael D&#237;az

Reputation: 2289

The output of R with the confusionMatrix () function is as follows

> confusionMatrix(xtab)
Confusion Matrix and Statistics

          truth
pred       abnormal normal
  abnormal      231     32
  normal         27     54

               Accuracy : 0.8285          
                 95% CI : (0.7844, 0.8668)
    No Information Rate : 0.75            
    P-Value [Acc > NIR] : 0.0003097       

                  Kappa : 0.5336          
 Mcnemar's Test P-Value : 0.6025370       

            Sensitivity : 0.8953          
            Specificity : 0.6279          
         Pos Pred Value : 0.8783          
         Neg Pred Value : 0.6667          
             Prevalence : 0.7500          
         Detection Rate : 0.6715          
   Detection Prevalence : 0.7645          
      Balanced Accuracy : 0.7616          

       'Positive' Class : abnormal

While using stargazer enter image description here

Upvotes: 1

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